{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014、2015年的预测结果分别是：\n",
      "10387002.56万元和12929795.07万元\n",
      "后验差比值为： 0.2390\n",
      "小残差概率： 1.00\n",
      "该模型精度为---好\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>3152063.0</td>\n",
       "      <td>2242747.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>2213050.0</td>\n",
       "      <td>2791784.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>4050122.0</td>\n",
       "      <td>3475227.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>5265142.0</td>\n",
       "      <td>4325981.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>5556619.0</td>\n",
       "      <td>5385003.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>4772843.0</td>\n",
       "      <td>6703281.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>9463330.0</td>\n",
       "      <td>8344279.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10387002.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12929795.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    y       y_pred\n",
       "2007-01-01  3152063.0   2242747.69\n",
       "2008-01-01  2213050.0   2791784.04\n",
       "2009-01-01  4050122.0   3475227.37\n",
       "2010-01-01  5265142.0   4325981.19\n",
       "2011-01-01  5556619.0   5385003.99\n",
       "2012-01-01  4772843.0   6703281.11\n",
       "2013-01-01  9463330.0   8344279.36\n",
       "2014-01-01        NaN  10387002.56\n",
       "2015-01-01        NaN  12929795.07"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #-*- coding:utf-8 -*-\n",
    "# 2_3 政府性基金收入预测模型\n",
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from GM11 import GM11 # 引入自己编的灰色预测函数\n",
    "\n",
    "x0 = np.array([3152063,2213050, 4050122, 5265142, 5556619, 4772843, 9463330])\n",
    "f, a, b, x00, C, P = GM11(x0)\n",
    "print (u'2014、2015年的预测结果分别是：\\n%0.2f万元和%0.2f万元' % (f(8), f(9)))\n",
    "print (u'后验差比值为： %0.4f' % C)\n",
    "print (u'小残差概率： %0.2f' % P)\n",
    "\n",
    "if (C < 0.35 and P > 0.95): # 评测后验差判别\n",
    "    print ('该模型精度为---好' )\n",
    "elif (C < 0.5 and P > 0.8):\n",
    "    print ('该模型精度为---合格' )\n",
    "elif (C < 0.65 and P > 0.7):\n",
    "    print ('该模型精度为---勉强合格')\n",
    "else:\n",
    "    print ('该模型精度为---不合格' )\n",
    "    \n",
    "p = pd.DataFrame(x0, columns = ['y'], index = range(2007,2014))\n",
    "p.loc[2014] = None\n",
    "p.loc[2015] = None\n",
    "p['y_pred'] = [f(i) for i in range(1,10)]\n",
    "p['y_pred'] = p['y_pred'].round(2)\n",
    "p.index = pd.to_datetime(p.index, format='%Y')\n",
    "\n",
    "p\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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eMweGDau2pAVpSlxmVg94BrjW3VM6wbI47imVqaqxanv/RSTGFi6Efv3gxBNh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\nEckoSlwiIpJR/j+51M6Fx0mkaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5316a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt # 画出预测结果图\n",
    "plt.rc('figure',figsize=(7,6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']= False\n",
    "\n",
    "# plt.title(u'灰色预测政府性基金收入真是值和预测值对比')\n",
    "p1 = p[['y','y_pred']].plot(subplots = True, style=['b-o', 'r-*'])\n",
    "# plt.xlabel(u'年份')\n",
    "# plt.ylabel(u'收入')\n",
    "plt.savefig('zhengfujijin.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EKCKyFB98EEv+3XRTlmY3++kn2Htv+OorePLJmERb8kqJUERkKW6+GVZYYdHk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3a2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot(xticks = p.index, style=['b-o', 'r-*'])\n",
    "plt.title(u'灰色预测政府性基金收入真是值和预测值对比')\n",
    "plt.xlabel(u'年份')\n",
    "plt.ylabel(u'收入')\n",
    "plt.savefig('zhengfujijin2.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
